20 M. Manikandan, M.Suguna

International Journal of Innovations & Advancement in Computer Science

IJIACS

ISSN 2347 – 8616

Volume 6, Issue 1

January 2017

A Survey on Temporal Task Scheduling for Profit

Maximization in Hybrid Clouds

M. Manikandan M.Suguna

M.E.,Scholar(CSE) Asst, Professor(GR-II)

Kumaraguru College Of Technology Kumaraguru College Of Technology

Coimbatore, India Coimbatore, India

ABSTRACT

Cloud Computing is a novel paradigm for the

provision of computing infrastructure, which aims to

shift the location of the computing infrastructure to the

network in order to reduce the costs of management and

maintenance of hardware and software resources. Cloud

computing has a service-oriented architecture in which

services are broadly divided into three categories:

Infrastructure-as-a- Service (IaaS), which includes

equipment such as hardware, Storage, servers, and

networking components are made accessible over the

Internet; Platform-as-a-Service (PaaS), which includes

hardware and software computing platforms such as

virtualized servers, operating systems, and the like and

Software-as-a-Service (SaaS), which includes software

applications and other hosted services.

To obtain accurate estimation of the complete

probability distribution of the request response time and

other important performance indicators. The model

allows cloud operators to determine the relationship

between the number of servers and input buffer size, on

one side, and the performance indicators such as mean

number of tasks in the system, blocking probability, and

probability that a task will obtain immediate service, on

the other. Therefore, it is possible that a private cloud

provider cannot satisfy all arrival tasks with its limited

resources if the arrival tasks are massive. The existing

works usually provide an admission control mechanism

to refuse some of arrival tasks that exceed the capacity of

a private cloud. Nevertheless, this will decrease the

throughput of a private cloud, and inevitably cause

revenue loss to the private cloud provider.

KEYWORDS: cloud computing ,datacenters, big data,

task scheduling

1.INTRODUCTION

Cloud computing can efficiently provide

on-demand computing resources over the network

to consumers worldwide. Typically, computing

resources in cloud data centers are dynamically

delivered to consumers using a pay-as-you-go

pricing model. In addition, the economy of scale

brought by cloud computing attracts an increasing

number of companies to deploy their applications in

cloud data centers. As a typical part of cloud,

Infrastructure as a Service (IaaS) provides the

foundation for applications. Typical IaaS providers

such as Rack space and AmazonEC2 provide

services to consumers based on a pay-per-use

model. An IaaS provider manages its own limited

resources. Therefore, similar to the definition from

the perspective of an IaaS provider, private cloud in

this paper denotes a resource-constrained IaaS

provider that may outsource some of its tasks to

execute in external public clouds when it cannot

deliver promised quality-of-service (QoS) with its

resources.

A private cloud provider aims to provide

services to consumers‘ tasks in the most cost-

effective way while guaranteeing the specified QoS.

Therefore, profit maximization is a critically

important goal for a private cloud provider. The

uncertainty and aperiodicity of arrival tasks makes

it difficult top redo the future arrival tasks, and

brings a major challenge to operators of a private

cloud. Therefore, it is possible that a private cloud

provider cannot satisfy all arrival tasks with its

limited resources if the arrival tasks are massive.

The existing works usually provide an admission

control mechanism to refuse some of arrival tasks

that exceed the capacity of a private cloud.

Nevertheless, this will decrease the throughput of a

private cloud, and inevitably cause revenue loss to

the private cloud provider. However, the

mechanism of hybrid clouds enables a private cloud

provider to make use of public clouds where

21 M. Manikandan, M.Suguna

International Journal of Innovations & Advancement in Computer Science

IJIACS

ISSN 2347 – 8616

Volume 6, Issue 1

January 2017

resources are delivered in the form of virtual

machines (VMs).

2.LITERATURE SURVEY

Linlin Wu, Saurabh Kumar Greg[3] porposed SLA-

Based Resource Provisioning for Hosted Software-

as-a-Service Applications in Cloud Computing

Environments

Cloud computing is a solution for addressing

challenges such as licensing, distribution,

configuration, and operation of enterprise

applications associated with the traditional IT

infrastructure, software sales and deployment

models. Migrating from a traditional model to the

Cloud model reduces the maintenance complexity

and cost for enterprise customers, and provides on-

going revenue for Software as a Service (SaaS)

providers. Clients and SaaS providers need to

establish a Service Level Agreement (SLA) to

define the Quality of Service (QoS). The main

objectives of SaaS providers are to minimize cost

and to improve Customer Satisfaction Level (CSL).

In this paper, we propose customer driven SLA-

based resource provisioning algorithms to minimize

cost by minimizing resource and penalty cost and

improve CSL by minimizing SLA violations. The

proposed provisioning algorithms consider

customer profiles and providers ‗quality parameters

(e.g., response time) to handle dynamic customer

requests and infrastructure level heterogeneity for

enterprise systems. We also take into account

customer-side parameters (such as the proportion of

upgrade requests), and infrastructure-level

parameters (such as the service initiation time) to

compare algorithms. Simulation results show that

our algorithms reduce the total cost up to 54 percent

and the number of SLA violations up to 45 percent,

compared with the previously proposed best

algorithm.

Dario Bruneo[4] introduced A Stochastic Model to

Investigate Data Center Performance and QoS in

IaaS Cloud Computing Systems .Cloud datacenter

management is a key problem due to the numerous

and heterogeneous strategies that can be applied,

ranging from the VM placement to the federation

with other clouds. Performance evaluation of Cloud

Computing infrastructures is required to predict and

quantify the cost-benefit of a strategy portfolio and

the corresponding Quality of Service (QoS)

experienced by users. Such analyses are not feasible

by simulation or on-the-field experimentation, due

to the great number of parameters that have to be

investigated. In this paper, we present an analytical

model, based on Stochastic Reward Nets (SRNs),

that is both scalable to model systems composed of

thousands of resources and flexible to represent

different policies and cloud-specific strategies.

Several performance metrics are defined and

evaluated to analyze the behavior of a Cloud data

center: utilization, availability, waiting time, and

responsiveness. A resiliency analysis is also

provided to take into account load bursts. Finally, a

general approach is presented that, starting from the

concept of system capacity, can help system

managers to opportunely set the data center

parameters under different working conditions.

A. Shahina Banu and W. R. Helen [5] introduced

Self-Adaptive Learning PSO-Based Deadline

Constrained Task Scheduling for Hybrid IaaS Cloud

Public clouds provide Infrastructure as a

Service(IaaS) to users who do not own sufficient

compute resources. IaaSachieves the economy of

scale by multiplexing, and therefore facesthe

challenge of scheduling tasks to meet the peak

demand whilepreserving Quality-of-Service (QoS).

Previous studies proposedproactive machine

purchasing or cloud federation to resolve

thisproblem. However, the former is not economic

and the latter fornow is hardly feasible in practice.

In this paper, we propose aresource allocation

framework in which an IaaS provider can out-

source its tasks to External Clouds (ECs) when its

own resourcesare not sufficient to meet the demand.

This architecture does notrequire any formal inter-

cloud agreement that is necessary for thecloud

federation. The key issue is how to allocate users‘

tasks tomaximize the profit of IaaS provider while

guaranteeing QoS. Thisproblem is formulated as an

integer programming (IP) model, andsolved by a

self-adaptive learning particle swarm optimization

(SLPSO)-based scheduling approach. In SLPSO,

four updatingstrategies are used to adaptively

update the velocity of each particle to ensure its

diversity and robustness. Experiments show that,

SLPSO can improve a cloud provider‘s profit by

0.25%–11.56%compared with standard PSO; and

by 2.37%–16.71% for problems of nontrivial size

compared with CPLEX under reasonable

computation time

22 M. Manikandan, M.Suguna

International Journal of Innovations & Advancement in Computer Science

IJIACS

ISSN 2347 – 8616

Volume 6, Issue 1

January 2017

Tejas Nitore ,Vishal Rale Ambi Talegaov[6] A

Profit Maximization Scheme with Guaranteed

Quality of Service in Cloud Computing. As an

effective and efficient way to provide computing

resources and services to customers on demand,

cloud computing has become more and more

popular. From cloud service providers‘ perspective,

profit is one of the most important considerations

,and it is mainly determined by the configuration of

a cloud service platform under given market

demand. However, a single long-term renting

scheme is usually adopted to configure a cloud

platform, which cannot guarantee the service

quality but leads to serious resource waste. In this

paper, a double resource renting scheme is designed

firstly in which short-term renting and long-term

rentingare combined aiming at the existing issues.

This double renting scheme can effectively

guarantee the quality of service of all requestsand

reduce the resource waste greatly. Secondly, a

service system is considered as

anM/M/m+Dqueuing model and the

performanceindicators that affect the profit of our

double renting scheme are analyzed, e.g., the

average charge, the ratio of requests that

needtemporary servers, and so forth. Thirdly, a

profit maximization problem is formulated for the

double renting scheme and the

optimizedconfiguration of a cloud platform is

obtained by solving the profit maximization

problem. Finally, a series of calculations are

conductedto compare the profit of our proposed

scheme with that of the single renting scheme. The

results show that our scheme can not onlyguarantee

the service quality of all requests, but also obtain

more profit than the latter.

Jianying Luo, Lei Rao, and Xue Liu[7] proposed

Temporal Load Balancing with Service Delay

Guarantees for Data Center Energy Cost

Optimization Cloud computing services are

becoming integral part of people‘s daily life. These

services are supported by infrastructure known as

Internet data center (IDC). As demand for cloud

computing services soars, energy consumed by

IDCs is skyrocketing. Both academia and industry

have paid great attention to energy management of

IDCs. This paper studies an important energy

management problem—how to minimize energy

cost for IDCs in deregulated electricity markets. We

propose a novel two-stage design and the eco-IDC

(Energy Cost Optimization-IDC) algorithm to

exploit the temporal diversity of electricity price

and dynamically schedule workload to execute on

IDC servers through an input queue. Extensive

evaluation experiments are performed using real-

life electricity price and workload traces at an

enterprise production data center. The evaluation

results demonstrate that the proposed approach

significantly reduces energy cost for IDCs,

guarantees a service delay bound, and alleviates

workload drop if the service delay bound is

sufficiently large.

Haitao Yuan, Jing Bi [8] introduced CAWSAC:

Cost-Aware Workload Scheduling and Admission

Control for Distributed Cloud Data Centers

Multiple heterogeneous applications concurrently

run in distributed cloud data centers (CDCs) for

better performance and lower cost. There is a highly

challenging problem of how to minimize the total

cost of a CDCs provider in a market where the

bandwidth and energy cost show geographical

diversity. To solve the problem, this paper first

proposes a revenue-based workload admission

control method to judiciously admit requests by

considering factors including priority, revenue and

the expected response time. Then, this paper

presents a cost-aware workload scheduling method

to jointly optimize the number of active servers in

each CDC, and the selection of Internet service

providers for the CDCs provider. Finally, trace-

driven simulation results demonstrate that the

proposed methods can greatly reduce the total cost

and increase the throughput of the CDCs provider in

comparison to existing methods.

Wenhong Tian[9]developed A Toolkit for Modeling

and Simulation of Real-time Virtual Machine

Allocation in a Cloud Data Center Resource

scheduling in infrastructure as a service (IaaS) is

one of the keys for large-scale Cloud applications.

Extensive research on all issues in real environment

is extremely difficult because it requires developers

to consider network infrastructure and the

environment, which may be beyond the control. In

addition, the network conditions cannot be

predicted or controlled. Therefore, performance

evaluation of workload models and Cloud

provisioning algorithms in a repeatable manner

under different configurations and requirements is

difficult.There is still lack of tools that enable

developers to compare different resource scheduling

23 M. Manikandan, M.Suguna

International Journal of Innovations & Advancement in Computer Science

IJIACS

ISSN 2347 – 8616

Volume 6, Issue 1

January 2017

algorithms in IaaS regarding both computing

servers and user workloads. To fill this gap in tools

for evaluation and modeling of Cloud environments

and applications, we propose CloudSched.

CloudSched can help developers identify and

explore appropriate solutions considering

differentresource scheduling algorithms. Unlike

traditional scheduling algorithms considering only

one factor such as CPU, which can cause hotspots

or bottlenecks in many cases, CloudSched treats

multi-dimensional resource such as CPU, memory

and network bandwidth integrated for both physical

machines and virtual machines for different

scheduling objectives (algorithms).In this paper,

two existing simulation systems at application level

for Cloud computing are studied, a novel

lightweight simulation system is proposed for real-

time virtual machine scheduling in Cloud data

centers, and results by applying the proposed

simulation system are analyzed and discussed.

Weijia Song, Zhen Xiao[10] proposed Adaptive

Resource Provisioning for the Cloud Using Online

Bin Packing Data center applications present

significant opportunities for multiplexing server

resources. Virtualization technology makes it easy

to move running application across physical

machines. In this paper, we present an approach that

uses virtualization technology to allocate data center

resources dynamically based on application

demands and support green computing by

optimizing the number of servers actively used. We

abstract this as a variant of the relaxed on-line bin

packing problem and develop a practical, efficient

algorithm that works well in a real system. We

adjust the resources available to eachVM both

within and across physical servers. Extensive

simulation and experiment results demonstrate that

our system achieves good performance compared to

the existing work.

Tan Lu and Minghua Chen [11] had worked on

Simple and Effective Dynamic Provisioning for

Power-Proportional Data Centers Energy

consumption represents a significant cost in data

center operation. A large fraction of the energy,

however, isused to power idle servers when the

workload is low. Dynamic provisioning techniques

aim at saving this portion of the energy, by turning

o_ unnecessary servers. In this paper, we explore

how much gain knowing future workload

information can bring to dynamic provisioning. In

particular, we develop online dynamic provisioning

solutions with and without future workload

information available. We first reveal an elegant

structure of the o_-line dynamic provisioning

problem, which allows us to characterize the

optimal solution in a ―divide-and-conquer‖ manner.

We then exploit this insight to design two online

algorithms with competitive ratios 2 � _ and e= (e

� 1 + _), respectively, where 0 _ _ _ 1 is the

normalized size of a look-ahead window in which

future workload information is available. A

fundamental observation is that future workload

information beyond the full-size look-ahead

window (corresponding to _ = 1) will not

improvedynamic provisioning performance. Our

algorithms are decentralized and easy to implement.

We demonstrate their effectiveness in simulations

using real-world traces.

Mohamed Faten Zhani [12] improved Dynamic

Heterogeneity-Aware Resource Provisioning in the

Cloud Data centers consume tremendous amounts

of energy in terms of power distribution and

cooling. Dynamic capacity provisioning is a

promising approach for reducing energy

consumption by dynamically adjusting the number

of active machines to match resource demands.

However, despite extensive studies of the problem,

existing solutions have not fully considered the

heterogeneity of both workload and machine

hardware found in production environments. In

particular, production data centers oftencomprise

heterogeneous machines with different capacities

and energy consumption characteristics.

Meanwhile, the production cloud workloads

typically consist of diverse applications with

different priorities, performance and resource

requirements. Failure to consider the heterogeneity

of both machines and workloads will lead to both

sub-optimal energy-savings and long scheduling

delays, due toincompatibility between workload

requirements and the resources offered by the

provisioned machines. To address this limitation,

we present Harmony, a Heterogeneity-Aware

dynamic capacity provisioning scheme for cloud

data centers. Specifically, we first use the K-means

clustering algorithm to divide workload into distinct

task classes with similar characteristics in terms of

resource and performance requirements. Then we

present a technique that dynamically adjusting the

number of machines to minimize total energy

24 M. Manikandan, M.Suguna

International Journal of Innovations & Advancement in Computer Science

IJIACS

ISSN 2347 – 8616

Volume 6, Issue 1

January 2017

consumption and scheduling delay. Simulations

using traces from a Google‘s compute cluster

demonstrate Harmony can reduce energy by 28

percent compared to heterogeneity-oblivious

solutions.

3.CONCLUSION

To design, implementation, and evaluation

of a resource management system for cloud

computing services our system multiplexes virtual

to physical resources adaptively based on the

changing demand. Present a system that uses

virtualization technology to allocate data center

resources dynamically based on application

demands and support green computing by

optimizing the number of servers in use. To use the

skewness metric to combine VMs with different

resource characteristics appropriately so that the

capacities of servers are well utilized. Algorithm

achieves both overload avoidance and green

computing for systems with multi resource

constraints. Proposing a new strategy that can be

included in the Cloud-Analyst to have cost effective

results and development and we can conclude from

the results that this strategy is able to do so.

From the work done, concluding that the

simulation process can be improved by modifying

or adding new strategies for traffic routing, load

balancing etc. to make researchers and developers

able to do prediction of real implementation of

cloud, easily. To develop a set of heuristics that

prevent overload in the system effectively while

saving energy used. Trace driven simulation and

experiment results demonstrate that our algorithm

achieves good performance. In the cloud model is

expected to make such practice unnecessary by

offering automatic scale up and down in response to

load variation. It also saves on electricity which

contributes to a significant portion of the

operational expenses in large data centers.

4.REFERENCES [1] R. Zou, V. Kalivarapu, E. Winer, J. Oliver, and S.

Bhattacharya, ―Par- ticle swarm optimization-based

source seeking,‖ IEEE Trans. Autom. Sci. Eng., vol.

12, no. 3, pp. 865–875, Jul. 2015.

[2] J. Bi, H. Yuan, M. Tie, and W. Tan, ―SLA-based

optimization of virtualized resource for multi-tier

web applications in cloud data centre‘s,‖ Enterprise

Inform. Syst., vol. 9, no. 7, pp. 743–767, Nov. 2015.

[3] Linlin Wu, Saurabh Kumar Greg[3] porposed SLA-

Based Resource Provisioning for Hosted Software-

as-a-Service Applications,‖ IEEE Trans. Services

Computer., vol. 7, no. 3, pp. 465–485, Jul. 2014.

[4] Dario Bruneo introduced A Stochastic Model to

Investigate Data Center Performance and QoS in

IaaS Cloud Computing Systems Proc. 32nd IEEE

Int.Conf. Comput. Commun., 2013, pp. 2148–2156.

[5] A. Shahina Banu and W. R. Helen introduced Self-

Adaptive Learning PSO-Based Deadline

Constrained Task Scheduling for Hybrid IaaS Cloud.

EEE Trans. Autom. Sci. Eng., vol. 12,no. 1, pp. 309–

323, Jan. 2014.

[6] Tejas Nitore ,Vishal Rale Ambi Talegaov A Profit

Maximization Scheme with Guaranteed Quality of

Service in Cloud Computing. International Journal

of Computer Applications (0975 – 8887) National

Conference on Advancements in Computer &

Information Technology (NCACIT-2016)

[7] Jianying Luo, Lei Rao, and Xue Liu proposed

Temporal Load Balancing with Service Delay

Guarantees for Data Center Energy Cost

Optimization. ,‖ IEEE Trans. Parallel Distrib. Syst.,

vol. 25, no. 3, pp. 775–784, March 2014.

[8] Haitao Yuan, Jing Bi introduced CAWSAC: Cost-

Aware Workload Scheduling and Admission Control

for Distributed Cloud Data Centers vol. 2, no. 1,

january-march 2014

[9] Wenhong Tian developed A Toolkit for Modeling

and Simulation of Real-time Virtual Machine

Allocation in a Cloud Data Center Resource

scheduling in infrastructure as a service (IaaS).

Future Gener. Comp. Sy., vol. 25, no. 6, pp. 599–

616, 2009.

[10] Weijia Song, Zhen Xiao proposed Adaptive

Resource Provisioning for the Cloud Using Online

Bin Packing Data center. High Performance

Distributed Computing.ACM, 2011, pp. 229–238.

[11] Tan Lu and Minghua Chen had worked on Simple

and Effective Dynamic Provisioning for Power-

Proportional Data Centers Energy consumption

represents a significant cost in data center operation.

IEEE Trans. Parallel Distrib. Syst., vol. 3, no. 3, pp.

775–784, Feb 2014.

[12] Mohamed Faten Zhani improved Dynamic

Heterogeneity-Aware Resource Provisioning in the

Cloud Data centers consume tremendous amounts of

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IEEE transactions on cloud computing, vol. 2, no. 1,

january-march 2014

25 M. Manikandan, M.Suguna

International Journal of Innovations & Advancement in Computer Science

IJIACS

ISSN 2347 – 8616

Volume 6, Issue 1

January 2017

5.ABOUT THE AUTHOR

Mr. M.Manikandan received his diploma in

Computer Science and engineering from Paavai

Institute and B.E Degree in Computer Science and

engineering from Bannari Amman Institute of

Technology, Erode, India. He is currently pursuing

M.E. Degree in Computer Science and Engineering

in Kumaraguru College of Technology,

Coimbatore, India. His areas of interest are Cloud

Computing , Big Data and Web Technology.

M.Suguna is a Asst.Professor(GR-II) in the

Department of Computer Science and Engineering,

Kumaraguru College of Technology, Coimbatore,

India. She received her M.E degree in Computer

Science and Engineering from Govt. College of

Technology in 2005. She has published several

papers in National / International Journals and

Conferences. Her current research interest includes

Cloud Computing, Software Project management.

She is a life member of Indian Society for Technical

Education.